{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:53:33Z","timestamp":1783529613372,"version":"3.55.0"},"reference-count":20,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003107","name":"German Federal Ministry of Health","doi-asserted-by":"publisher","award":["2520DAT920"],"award-info":[{"award-number":["2520DAT920"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been shown that windowing improves classification performance on computed tomography (CT) images, the impact of such an operation on CXR classification performance remains unclear. In this study, we show that windowing strongly improves the CXR classification performance of machine learning models and propose WindowNet, a model that learns multiple optimal window settings. Our model achieved an average AUC score of 0.812 compared with the 0.759 score of a commonly used architecture without windowing capabilities on the MIMIC data set.<\/jats:p>","DOI":"10.3390\/jimaging9120270","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T03:48:41Z","timestamp":1701834521000},"page":"270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["WindowNet: Learnable Windows for Chest X-ray Classification"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9535-0502","authenticated-orcid":false,"given":"Alessandro","family":"Wollek","sequence":"first","affiliation":[{"name":"Munich Institute of Biomedical Engineering, TUM School of Computation, Information, and Technology, Technical University of Munich, 80333 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sardi","family":"Hyska","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Ludwig-Maximilians-University, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastian","family":"Sabel","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Ludwig-Maximilians-University, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Ingrisch","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Ludwig-Maximilians-University, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-920X","authenticated-orcid":false,"given":"Tobias","family":"Lasser","sequence":"additional","affiliation":[{"name":"Munich Institute of Biomedical Engineering, TUM School of Computation, Information, and Technology, Technical University of Munich, 80333 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","article-title":"MIMIC-CXR, a de-Identified Publicly Available Database of Chest Radiographs with Free-Text Reports","volume":"6","author":"Johnson","year":"2019","journal-title":"Sci. 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